The Markov Chain Monte Carlo (MCMC) is the most commonly used method to estimate the best-fit parameters from the multidimensional parameter space of cosmological data. The posterior probability distribution is created using the Bayesian approach. For sampling the posterior, MCMC algorithms used one of the most widely used samplers called the Metropolis-Hastings (MH) [1]}, [2]} sampler. The MH algorithm draws the sample based on a random walk over the likelihood of the parameter space by serially proposing new accepted or rejected locations based on the likelihood weights. The MH algorithm generally considers the target distribution and the proposal conditional distribution from which a candidate sample for the new Markov chain state is drawn. The disadvantage of using MH samplers is that they typically require several thousands of model evolutions, and only a small portion gets accepted. Hence, applying the algorithm to problems where the model evaluation is computationally time-consuming is challenging. The intrinsic serial nature of the MH chains often takes a long time to map the posterior. Although MH delivers the intended result, it may struggle to do so efficiently if a suitable proposal function is challenging to discover or if the assumption of a fixed proposal function proves to be inefficient. Several other sampling algorithms are available, which may be more robust than the MH algorithms, for example, Slice sampling [3]}, Gibbs sampling [4]}, [5]}, [6]}, Hamiltonian sampling [7]}, [8]} .
